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This issue of the journal "Atmospheric and Oceanic Optics" includes articles based on papers presented at the International Symposium on Atmospheric Radiation and Dynamics (ISARD-2025).

Atmospheric aerosols are a significant factor of variations in the radiative balance, particularly for such regions as Central Siberia, where there are many anthropogenic and biogenic aerosol sources. However, the parameters and seasonal dynamics of aerosol radiative forcing in this region remain understudied. The aim of this work is to estimate the efficiency of aerosol radiative forcing ( RFE ) for the atmosphere of Central Siberia based on measurements of aerosol scattering and absorption coefficients at background ZOTTO station in 2007-2024. The atmospheric and underlying surface characteristics required for calculating RFE were taken from MERRA-2 reanalysis data. The resulting time series of RFЕ for ZOTTO station show strong day-to-day variability and a clearly pronounced seasonal cycle. Although the maximal concentrations of absorbing (soot) aerosol and, consequently, the maximal values of the aerosol absorption coefficient are observed in summer, the efficiency of aerosol forcing during this period is negative, with the characteristic RFЕ = -30 W/m². In winter, when aerosol concentrations and aerosol optical coefficients are substantially lower, the efficiency of aerosol forcing is positive and amounts to approximately +25 W/m²; the measurement-period mean RFE = -5 W/m². The change in the sign of aerosol forcing from positive to negative occurs in early May, and vice versa, in late October, which is primarily due to the seasonal change in the albedo of the underlying surface. The results can be used to refine predictions of regional climate changes in Siberia.

Studying the spatial distribution and temporal variation in CH₄ concentration as a greenhouse gas is a relevant but difficult scientific task in the Arctic. Reanalysis data can serve an additional source of information, but they require regular validation. This study presents the results of an assessment of the reproduction of surface CH₄ concentration by CAMS global greenhouse gas reanalysis database version EGG4 in the Arctic region. Reanalysis data on the surface CH₄ concentrations variations are compared with continuous measurements at the research station “Ice Base Cape Baranova" (79°16' N, 101°45' E) in 2016-2020 on different time scales (interannual, seasonal, daily). It is found that reanalysis data reflect the interannual variability of surface CH₄ concentrations the worst. The seasonal variability of the CH₄ concentration is well described by the reanalysis data, the model amplitudes of the seasonal cycle are slightly higher than the actual ones. The comparison of the model and actual values of surface temperature and wind speed and direction are also carried out. Such verification of the CAMS database is useful before its subsequent using in regional-scale numerical modeling and other applied problems.

A method for remote mapping of the sea surface temperature field (SST) in a cloudless and cloudy atmosphere has been proposed and tested based on SST measurements of MTVZA-GYa microwave radiometer from Meteor M satellite Nos. 2-2 and 2-4. The method includes the preliminary SST estimation using an artificial neural network (ANN) algorithm of multilayer perceptron type and statistical filtering of the preliminary estimates using climatic SST values calculated from the ERA5 reanalysis. The neural network algorithm uses antenna temperatures measured in five scanner channels of the MTVZA-GYa radiometer as predictors. Reference SST values from the open access ICOADS database are used to train the ANN. The statistical filtering procedure makes it possible to reduce the influence of clouds and precipitation in the satellite radiometer field of view and provides a root-mean-square error of the SST estimates on the order of 1.2-1.7 °C and a coefficient of determination of about 0.8-0.9 when compared with in situ observations. The proposed approach is applicable to operational global “all-weather" monitoring of sea surface temperature and can be adapted to analyze SST measurements of MTVZA-GYa type radiometers with improved technical and information characteristics.

This article addresses the problem of cloud detection in hyperspectral satellite images using an interpretable neural network classifier for partial cloudiness. For effective solution, the preliminary selection of spectral channels and derived features is performed using decision trees trained with labeled satellite data of the HYPERION sensor. The selected channels and features are then used for building a convolutional neural network based on a modified Unet architecture. Modifications to the original Unet architecture enable simplifying the network structure, avoiding overfitting, assessing the importance of spatial and spectral features, analyzing classification results, and explaining decision-making processes. Feature selection and evaluation of their importance are critical stages in developing adequate machine learning and deep learning models combined with the analysis of their generalization ability. The suggested feature selection method is based on iterative training of decision trees to identify significant features in terms of classification accuracy. The operation of the convolutional neural network is interpreted and the importance of spatial and spectral features is assessed by evaluating Shapley vectors. The results of testing a neural network with HYPERION images made over three surface types (ocean, vegetation, and urbanized territory) are presented; its accuracy and commission and omission errors are estimated. The model enables semantic segmentation of images with thin clouds with accuracy over 95% in selected spectral bands and with selected features. The importance of input features, caused by their distribution across spectral channels and the relative positions of pixels in an image, for the detection of thick and thin clouds in hyperspectral satellite images is analyzed. The presented neural network model is designed for working with limited data volumes, enables applying augmentation, and can be used to assess the importance of selected spectral channels and spatial features.

Soot aerosol from forest fires injected into the stratosphere can influence climate on a global scale, similar to volcanic aerosol. This paper examined the stratospheric loading with soot aerosol from forest fires in Eastern Siberia, as well as the occurrence of volcanic aerosol over Western Siberia. An episode of ground-based lidar observation in July 2022, where aerosol layers were detected in the stratosphere above Tomsk at approximately 11 km and 20-25 km is considered. The origin of these layers is analyzed using air mass trajectories with control of their aerosol content based on data from the CALIPSO satellite lidar and using atmospheric and surface sensing data from the Suomi-NPP and Himawari-8 satellites. It is shown that the sources of in the lower stratosphere aerosol loading at an altitude of about 11 km are the fires in Eastern Siberia, which led to the formation of powerful pyrocumulative clouds. The location and time of formation of these clouds are determined. It is also shown that the aerosol layers at altitudes 20-25 km are associated with the eruption of the Hunga Tonga-Hunga Ha'apai volcano, which erupted in the Southern Hemisphere in January 2022. The results are of significant interest for predicting climate change on regional and global scales.

In order to study the dynamics of the Earth's atmosphere, it is of interest to examine the frequency content of geomagnetic field variations in the range of the sixteen-day atmospheric planetary wave period (from 14.5 to 18 days). The spectra of Earth's magnetic field variations recorded between 2000 and 2023 at three European mid-latitude magnetic observatories, the Belsk Observatory (eastern Europe), the Furstenfeldbruck Observatory (central Europe), and the Dourbes Observatory (western Europe), were analyzed. Using the Lomb-Scargle periodogram method, harmonics associated with the modulation effect of long-period variations and tidal effects were identified in the spectrum in the range from 14.5 to 18 days. The analysis showed that the spectral content of geomagnetic variations does not depend on the longitude of the observation point (the points are located at approximately the same latitude). Spectral harmonics caused by the modulation wave with a semiannual variation of the second harmonic of the sunspot rotation cycle and the declination tidal were identified. For the M_sf tidal wave, harmonics were identified due to the modulation effect of the 11-year solar activity cycle (Schwabe), the fourth harmonic of the 22-year solar activity cycle, and annual and semiannual variations. Spectral harmonics are clearly distinguished in the spectra, whose periods correspond to the modulation effect of the 11-year solar activity cycle, the fourth harmonic of the 22-year solar activity cycle, and annual and semiannual variations on the 16-day planetary wave. The spectral analysis results confirm the influence of processes observed in the lower neutral atmosphere on the dynamics of the upper atmosphere. The results can be used to develop atmospheric dynamics models.

The study of large-scale atmospheric processes, such as planetary waves, plays a crucial role in understanding the coupling between the lower and upper layers of the atmosphere. However, accurate modeling of these waves is challenging due to the heterogeneity and sparsity of observational data (both satellite and ground-based), as well as the high dimensionality of the parameter space when describing wave structures. In this paper, an approach to approximation of planetary waves with the use of the Two Strategy adaptive Artificial Bee Colony (TSaABC) algorithm based heterogeneous satellite and ground-based data is suggested. The TSaABC algorithm is used to optimize the parameters of a nonlinear spatiotemporal model representing atmospheric temperature data obtained from the Aura satellite (MLS) and three ground-based stations measuring hydroxyl OH(3, 1) emission bands. The temperature data are approximated using the sum of planetary wave harmonics with unknown parameters including amplitudes and wavenumbers, which are selected from a dictionary of harmonics. By solving the inverse problem of minimizing the data divergence and the L₁-norm of harmonic amplitudes, the method achieves approximation accuracy and sparsity in a large dictionary of harmonics. To solve the L1-minimization problem, a hard thresholding strategy was developed within the TSaABC algorithm. The use of a hard threshold value allows us to reduce the dimensionality of the solution search, thus inereasing computational efficiency. The results demonstrate the potential of the algorithm for assimilating heterogeneous data and improving the modeling of atmospheric processes.

Introduction. The relevance of this study stems from the need for a methodological understanding of the integration of artificial intelligence (AI) technologies into education in response to the global challenges of the 21st century. The aim of this article is to analyze the evolution and legal consolidation of the concept of lifelong education in Russia and to substantiate its role as a systemic foundation for the implementation of AI and industrial engineering. Methodology. The study is based on a retrospective analysis of regulatory documents and scientific literature, as well as a systemic and comparative analysis to identify the relationship between the principles of lifelong education and technological solutions. Discussion. It is shown that the legislatively enshrined principle of continuity, implemented through formal, non-formal, and informal education, generates a methodological demand for personalization, flexibility, and accessibility. Five key contemporary challenges (speed of change, personalization, format integration, accessibility, and goal setting) are identified. AI offers an instrumental response, while industrial engineering serves as an operational mechanism for personalization and a new educational literacy. Conclusion. It is concluded that the concept of lifelong learning is not only a legal principle but also a necessary methodological framework that provides meaning and direction for technological integration. In accordance with the concept of lifelong learning, artificial intelligence ceases to be an accompanying technological attribute, becoming the architectural core of a new model of lifelong learning. AI tools provide the necessary mechanisms for implementing its core principles: adaptability, personalization, integration, and inclusiveness. However, the effectiveness of human interaction with this complex technological environment is determined by mastery of a new tool-industrial engineering. Research prospects include developing industrial engineering didactics and creating pedagogical models where AI serves as the architectural core of a system supporting individual educational trajectories throughout life.

Introduction. Large-scale international relations determine the adjustment of the forms and content of the educational process. The results of the ongoing transformations are new social, ethical, axiological, political and other aspects that have a significant impact on the fundamental knowledge development and the transmission of socially significant information. In this regard, the possibilities of using humanitarian education as a key factor determining the vector of dynamic processes of “soft power” in the social processes context are analyzed. The research methodology includes a structural and functional approach to the analysis of the “soft power” concept based on an analytical and descriptive methodology, which outlines the prospects for the higher education development through the soft-power influence of humanitarian education. Discussion. “Soft power” is a specific mechanism that can have an indirect effect on social relations and at the same time act as a communicative factor. It should be noted that the humanities have a distinctive feature in comparison with applied disciplines, since the main subject is the person himself, involved in a complex system involving various connections and relationships. Detachment from precise structures indicates the presence of “soft power” both in the interaction of humanitarian disciplines and in relation to the external environment, exerting a direct influence on an individual or a social group. In this context, the study of the humanities from the “soft power” point of view should be considered in a wide range of influences on the moral, social and spiritual activities of the individual. Conclusion. Based on the conducted research, the basic principles of the “soft power” of humanitarian education have been identified. The fundamental ones are communication skills, reasonableness and ideological orientations. Thus, if it turns out to be possible to expand the approach in which the importance of humanitarian education will grow rapidly, then this will contribute to the expansion of the “soft power” that can strengthen the complex of social values.